化学结构感知分子图像表示学习化学结构感知分子图像表示学习
Hongxin Xiang1,2, Shuting Jin3,4,2, Xiangrong Liu4,5
1School of Information Science and Engineering, Hunan University, Hunan, 410082, China.
Briefings in bioinformatics
|November 17, 2023
概括
这项研究引入了一种用于药物发现中的分子图像分析的新方法,使计算机能够从没有标签的图像中理解化学结构. 这种方法显著改善了分子表示学习和药物发现过程.
科学领域:
- 计算化学是一种计算化学.
- 机器学习是机器学习.
- 药物发现 药物发现
背景情况:
- 基于分子图像的药物发现面临着未标记的数据和从图像中提取化学结构的挑战.
- 目前的方法很难弥合隐性图像信息和显式化学结构编码之间的差距.
研究的目的:
- 开发一种用于分子表示学习的新框架,有效地将化学知识从分子图表转移到图像.
- 为了使分子图像编码器能够在没有明确标签的情况下感知和解释化学结构.
主要方法:
- 提出了一个对比图像预训练 (CGIP) 框架,利用自我监督的对比学习.
- 采用内部和跨模式的对比学习来从大规模的未标记分子中提取显式图形信息和隐式图像信息.
- 杆分子图表编码明确的化学结构 (例如,环,双键).
主要成果:
- 在12个基准数据集中,在分子性质预测,跨模式检索和分布相似性任务中实现了最先进的性能.
- 证明成功地将化学知识从图表转移到图像,允许图像编码器识别化学结构.
- 验证了框架在从未标记的分子数据中学习的有效性.
结论:
- 对比图形图像预训 (CGIP) 框架有效地弥合了分子图形和图像之间的差距,用于表示学习.
- CGIP使图像编码器能够解释化学结构,从而推进基于分子图像的药物发现.
- 这种方法突出了利用分子图像在化学中增强代表性学习的潜力.
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